Package 'fixes'

Description

Estimates average treatment effects on the treated (ATT) using either the Callaway-Sant'Anna (2021) or Borusyak-Jaravel-Spiess (2024) estimator, aggregated to a single summary effect ("simple"), per-cohort effects ("by_cohort"), or per calendar-time effects ("by_time").

Usage

att(
  formula,
  data,
  time,
  timing = NULL,
  unit = NULL,
  estimator = c("cs", "bjs"),
  aggregation = c("simple", "by_cohort", "by_time"),
  control_group = c("nevertreated", "notyettreated"),
  anticipation = 0L,
  conf_level = 0.95,
  interval = 1,
  time_transform = FALSE,
  outcome
)

Arguments

Details

This function complements event_study(): use event_study() when you want a full event-study curve (dynamic effects by relative time), and att() when you want aggregated ATT estimates that collapse the time dimension. Both take the same formula, and treatment is given in the same three ways (see event_study(), Specifying treatment):

att(y ~ D | id + year, data = df, time = year)                 # D column
att(y ~ 1 | id + year, data = df, time = year, timing = g)     # timing column
att(y ~ treated | id + year, data = df, time = year, timing = 2005)

The result has broom::tidy() and broom::glance() methods, so it can be passed to modelsummary::modelsummary() (rendered with tinytable) to build a publication table — including stacking several estimators (e.g. did() TWFE, CS, and BJS) side by side in one table.

att() supersedes calc_att() as of fixes 1.0.0.

Value

A data.frame of class "att_result" with columns:

group

Cohort or calendar time (NA for "simple").

estimate

ATT point estimate.

std.error

Standard error.

statistic

t-statistic (estimate / std.error).

p.value

Two-sided p-value (normal approximation).

conf_low_XX, conf_high_XX

CI bounds for each conf.level.

Attributes: aggregation, estimator, conf.level, N, N_units, N_treated, N_nevertreated, unit_var, time_var, control_group (CS only), att_gt (CS raw ATT(g,t) table), tau_it (BJS unit-time effects table).

Aggregation formulas (CS estimator)

• simple: $\theta = \sum_g (n_g/n_{treated}) \cdot \overline{ATT(g,\cdot)}$ where $\overline{ATT(g,\cdot)}$ is the mean over post-treatment periods.

• by_cohort: $\theta(g) = \overline{ATT(g,\cdot)}$ per cohort.

• by_time: $\theta(t) = \sum_{g \le t} w(g,t) \cdot ATT(g,t)$ with $w(g,t) = n_g / \sum_{g' \le t} n_{g'}$.

Rows with a missing or non-finite outcome, time, or unit are dropped and the panel is balanced by dropping units not observed in every period, as did::att_gt() does by default; both steps warn, and N, N_units, N_treated and N_nevertreated describe the balanced sample.

Standard errors

For "cs", the SE of each aggregate is computed from its unit-level influence function, which carries the covariance across the ATT(g,t) it averages and the estimation of the cohort weights (the same SEs as did::aggte()).

For "bjs", the SE is the conservative cluster-robust SE of Borusyak et al. (2024, Theorem 3), clustered by unit, including the uncertainty from the imputation regression. It matches didimputation::did_imputation().

See Also

event_study() for event-study (dynamic) estimates, and calc_att() for the deprecated verb-style predecessor.

Examples

## Not run: 
att(y ~ 1 | id + year, data = df, time = year, timing = g)
att(y ~ D | id + year, data = df, time = year,
    estimator = "bjs", aggregation = "by_cohort")

## End(Not run)

Description

Returns the cohort-by-period ATT(g,t) table computed by event_study(estimator = "cs") or att(estimator = "cs") as a real att_gt_result object with its own plot() and autoplot() methods. Bootstrap metadata (when the originating call used bootstrap = TRUE) is carried along so simultaneous-inference overlays keep working.

Usage

att_gt(x)

Arguments

Value

A data frame of class c("att_gt_result", "data.frame") with columns g (cohort), t (calendar time), estimate, and std_error.

See Also

plot.att_gt_result(), event_study(), att()

Examples

## Not run: 
res <- event_study(y ~ 1 | id + year, data = df, time = year, timing = g,
                   estimator = "cs")
gt <- att_gt(res)
plot(gt)                  # heatmap
plot(gt, type = "facet")  # one panel per cohort

## End(Not run)

Description

S3 method that plots an es_result (from event_study()). It forwards arguments to plot.es_result().

Usage

## S3 method for class 'es_result'
autoplot(
  object,
  ci_level = 0.95,
  type = c("errorbar", "ribbon"),
  time_axis = c("relative", "calendar"),
  ...
)

Arguments

Value

A ggplot object.

Examples

# res <- event_study(...)
# ggplot2::autoplot(res, ci_level = 0.95, type = "ribbon")

Description

Estimates the contamination weights $\omega^{\ell''}_{e,\ell}$ that decompose each TWFE event-study coefficient $\hat\mu_{\ell''}$ into a linear combination of cohort-specific ATTs (CATTs):

$\hat\mu_{\ell''} = \sum_{(e,\ell)} \omega^{\ell''}_{e,\ell} \cdot \widehat{CATT}_{e,\ell}$

(Sun and Abraham 2021, Equation 20).

The weights are obtained via the OVB auxiliary regression (Eq. 12): for each cohort-period CATT cell $(e, \ell)$, regress the cohort-specific indicator $1\{E_i = e\}\cdot 1\{t-E_i = \ell\}$ on all cohort-aggregated relative-time indicators $D^{\ell''}_{i,t}$ and two-way fixed effects. The resulting regression coefficient on $D^{\ell''}_{i,t}$ is $\omega^{\ell''}_{e,\ell}$.

Usage

contamination_weights(data, time, timing, unit, fe = NULL, baseline = -1L)

Arguments

Value

An object of class c("sa_contamination_weights", "data.frame") with one row per ⁠(catt_cohort, catt_period, twfe_period)⁠ triple, and columns:

catt_cohort

Cohort $e$ (first treatment period).

catt_period

Relative event time $\ell$ of the CATT.

twfe_period

Relative event time $\ell''$ of the TWFE coefficient being decomposed.

weight

Contamination weight $\omega^{\ell''}_{e,\ell}$.

is_own

Logical; TRUE when catt_period == twfe_period.

Attributes: baseline, cohorts, cohort_sizes, incl_periods.

Interpretation

• Own-period cell (catt_period == twfe_period): $\omega^{\ell}_{{e,\ell}}$ represents the weight the TWFE estimator places on $CATT_{e,\ell}$. Under the SA IW estimator these equal the cohort-size weights $n_e / \sum n_{e'}$.

• Cross-period cell (catt_period != twfe_period): Any non-zero weight indicates contamination: the TWFE coefficient $\hat\mu_{\ell''}$ also picks up treatment effects from period $\ell \ne \ell''$.

• Verification: the OVB identity (property iii) holds exactly, so $\hat\mu_{\ell''} = \sum_{(e,\ell)} \omega^{\ell''}_{e,\ell} \cdot \widehat{CATT}_{e,\ell}$ up to floating-point precision.

References

Sun, L. and Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175–199.

See Also

plot.sa_contamination_weights(), event_study(), and compute_contamination_weights() for the deprecated predecessor.

Examples

## Not run: 
# Estimate contamination weights
cw <- contamination_weights(
  data     = panel_data,
  time     = year,
  timing   = first_treat,
  unit     = id,
  fe       = ~ id + year,
  baseline = -1L
)
print(cw)
plot(cw)

## End(Not run)

Description

Estimates a classic difference-in-differences model of the form ⁠outcome ~ D + covariates | fixed effects⁠ on the package's internal fixed-effects OLS engine (numerically equivalent to fixest::feols()). The formula is the one used by event_study() and att(): swap the function to get effects by event time or aggregated staggered ATTs.

did() supersedes run_did() as of fixes 1.0.0.

Treatment is given in one of three ways, as in event_study():

# D: 1 for treated units in post-treatment periods
did(y ~ D | id + year, data = df)

# each unit's first treated period (NA = never treated)
did(y ~ 1 | id + year, data = df, time = year, timing = g)

# a treated-group dummy and one treatment date
did(y ~ treated | id + year, data = df, time = year, timing = 2006)

With timing, D = treatment * (time >= timing) is built internally. For staggered-adoption settings see att(); for dynamic effects see event_study().

Usage

did(
  formula,
  data,
  time = NULL,
  timing = NULL,
  unit = NULL,
  cluster = NULL,
  weights = NULL,
  conf_level = 0.95,
  vcov = "HC1",
  vcov_args = list(),
  outcome,
  treatment,
  fe = NULL,
  covariates = NULL
)

Arguments

Value

A did_result object (named list) with elements:

estimates

Data frame with the treatment coefficient: term, estimate, std.error, statistic, p.value, and conf_low_XX/conf_high_XX for each conf.level entry.

coeftable

Data frame with all kept coefficients (treatment and covariates): term, estimate, std.error, statistic, p.value.

vcov

Full coefficient covariance matrix.

fit_stats

Model-level statistics (nobs, R-squareds, sigma, logLik, AIC, BIC) used by broom::glance().

df.t

Degrees of freedom of the t inference.

Attributes: call, formula_str, outcome, treatment, timing, fe, vcov_type, cluster_vars, conf.level, N, N_units, N_treated, unit, time (and timing_var with a timing column). N, N_units, and N_treated describe the estimation sample (after rows with missing values are dropped), matching broom::glance(), not nrow(data).

See Also

event_study() for dynamic effects, att() for staggered aggregation, and run_did() for the deprecated verb-style predecessor.

Examples

## Not run: 
res <- did(y ~ D | id + year, data = df)

# Built from a treated-group dummy and the treatment date
res <- did(y ~ treated | id + year, data = df, time = year, timing = 2006)

# Covariates and cluster-robust SE
res <- did(y ~ D + x1 | id + year, data = df, cluster = ~ id)

print(res)
broom::tidy(res)
broom::glance(res)
# modelsummary::modelsummary(res)

## End(Not run)

Description

Estimates dynamic treatment effects by relative time (an event-study curve) on panel data. Supports the classic TWFE design (universal or staggered timing) plus modern staggered-robust estimators — Callaway-Sant'Anna, Sun-Abraham, Borusyak-Jaravel-Spiess, Wooldridge two-way Mundlak, and Deb et al. FLEX — selected via ⁠estimator=⁠. Estimation runs on the package's internal fixed-effects OLS engine; results are returned as a tidy table with rich metadata.

Usage

event_study(
  formula,
  data,
  time,
  timing = NULL,
  unit = NULL,
  estimator = c("twfe", "cs", "sa", "bjs", "twm", "flex"),
  cluster = NULL,
  weights = NULL,
  baseline = -1L,
  lead_range = NULL,
  lag_range = NULL,
  conf_level = 0.95,
  vcov = "HC1",
  vcov_args = list(),
  control_group = c("nevertreated", "notyettreated"),
  anticipation = 0L,
  bootstrap = FALSE,
  boot_reps = 999L,
  boot_alpha = 0.05,
  boot_seed = NULL,
  group = NULL,
  trends = FALSE,
  interval = 1,
  time_transform = FALSE,
  rel_time = NULL,
  staggered = NULL,
  outcome,
  treatment = NULL,
  fe = NULL,
  covariates = NULL
)

Arguments

Details

The model is a formula, ⁠outcome ~ treatment + covariates | fixed effects⁠, as in fixest::feols(); the same formula works in did() and att().

event_study() supersedes run_es() as of fixes 1.0.0.

How relative (event) time is defined. event_study() can build the event-time axis in three different ways, and in sparse or unbalanced panels (e.g. monthly surveys observed only a few times a year) they do not coincide:

In a balanced panel all three agree. When observation spacing is irregular they diverge: for a unit observed at periods ⁠{1, 5, 9, 20}⁠ treated at 20, the calendar difference puts period 9 at 9 - 20 = -11, whereas a global observed-period grid (as used by paneltools/fect's get.cohort()) places it two observed waves before treatment (-2). To reproduce an analysis built on such a grid, pass that grid's event-time column via rel_time so the design matches fixest::feols(y ~ i(rel_time, treatment, ref) | fe) exactly. att() uses the same calendar-difference convention as the default here. With a D column, a unit's start is the first period in which D = 1 is observed.

Missing values and unbalanced panels. With estimator = "cs", rows with a missing or non-finite outcome, time, or unit are dropped, and the panel is then balanced by dropping every unit not observed in all periods, as did::att_gt() does by default (allow_unbalanced_panel = FALSE). Both steps warn, and N, N_units, N_treated and N_nevertreated describe the balanced sample. The regression-based estimators drop rows with missing values from the fit as fixest::feols() does; with estimator = "sa" or "twm" the cohort shares used to aggregate cohort-specific effects count only units that remain in the estimation sample (Sun and Abraham 2021, Section 4.1).

Value

A data.frame of class "es_result" with columns:

• term, estimate, std.error, statistic, p.value

• conf_low_XX, conf_high_XX (for each requested conf.level)

• relative_time (integer; 0 = event), is_baseline (logical; marks the reference period)

Attributes include: lead_range, lag_range, baseline, interval, call, model_formula, conf.level, N, N_units, N_treated, N_nevertreated, fe, vcov_type, cluster_vars, staggered, sunab_used, unit_var, time_var.

Specifying treatment

Describe treatment in whichever way matches your data:

• D column (no timing): the first right-hand-side term is D. A unit's treatment starts in the first period with D = 1 and must stay on from then on (staggered adoption). Rows with a missing D are dropped. A single start date across units gives the classic universal design, several give the staggered one. With time_transform = TRUE the start is read on the time index, so no index arithmetic is needed.

• Timing column (timing = g): g defines treatment, so the right-hand side holds only covariates (write y ~ 1 | id + year when there are none). D or the treated-group dummy must not appear in the formula.

• Treated-group dummy and a date (timing = 2005): the first term is the treated-group dummy (constant within units), and treatment starts at timing for every treated unit.

A pre-built event-time column goes in rel_time, with the treated-group dummy as the first term: ⁠y ~ treated | id + year, time = year, rel_time = ttt⁠.

Panel identifiers

time is required. unit is taken from the fixed effects when they are plain columns made of time and exactly one other column (⁠| id + year⁠ with time = year, in either order); a message then reports the unit and time used. Otherwise pass unit: it is needed for a D column and for estimator = "cs", "sa", "bjs" and "twm". print() shows the identifiers of every result.

Fixed effects and covariates by estimator

"twfe", "sa" and "twm" use the fixed effects as written. "cs", "bjs" and "flex" absorb the unit (group) and time effects themselves: those fixed effects are accepted silently, any others are ignored with a warning. Covariates are supported by "twfe", "twm" and "flex", and may be expressions such as log(x) or factor(region); "cs", "sa" and "bjs" stop with an error when the formula contains covariates.

Key Features

• One formula for the model (outcome, treatment, covariates, fixed effects), shared with did() and att().

• Six estimators behind one interface: "twfe", "cs", "sa", "bjs", "twm", "flex".

• Flexible clustering, weights, and VCOV choices (e.g., ⁠vcov = "HC1" | "iid" | "hetero" ...⁠).

• Automatic lead/lag window detection and customizable baseline period.

• Returns an "es_result" object compatible with plot(), print(), and autoplot().

See Also

plot.es_result() for visualization, att() for aggregated ATT estimation, and run_es() for the deprecated verb-style predecessor.

Examples

## Not run: 
# D column: 1 for treated units in post-treatment periods
res <- event_study(y ~ D | id + year, data = df, time = year)
plot(res)

# Timing column (NA = never treated), Callaway-Sant'Anna
res <- event_study(y ~ 1 | id + year, data = df, time = year, timing = g,
                   estimator = "cs")

# Treated-group dummy and a single treatment date, clustered by unit
res <- event_study(y ~ treated + x1 | id + year, data = df, time = year,
                   timing = 2005, cluster = ~ id)

## End(Not run)

Description

Returns a one-row summary of an att() result for the goodness-of-fit block of a modelsummary::modelsummary() table.

Usage

glance.att_result(x, ...)

Arguments

Value

A one-row data frame with nobs, n.units, n.treated, estimator, and aggregation.

Examples

## Not run: 
res <- att(y ~ D | id + year, data = df, time = year)
broom::glance(res)

## End(Not run)

Description

Returns a single-row summary of model-level statistics from a did() result: r.squared, adj.r.squared, within.r.squared, sigma, nobs, AIC, BIC, and logLik, in the column layout of broom::glance() on a fixest model.

Usage

glance.did_result(x, ...)

Arguments

Value

A one-row tibble of model-level statistics.

Examples

## Not run: 
res <- did(y ~ D | id + year, data = df)
broom::glance(res)

## End(Not run)

Description

Returns a one-row summary of an event_study() result: sample sizes, estimator, and VCOV type.

Usage

glance.es_result(x, ...)

Arguments

Value

A one-row tibble with columns nobs, n_units, n_treated, n_nevertreated, estimator, and vcov_type.

Examples

## Not run: 
res <- event_study(y ~ 1 | id + year, data = df, time = year, timing = g,
                   estimator = "cs")
broom::glance(res)

## End(Not run)

Description

Robust inference and sensitivity analysis for event-study / DiD designs following Rambachan and Roth (2023). Instead of assuming parallel trends holds exactly, it asks how large a violation of parallel trends would have to be before the causal conclusion changes, returning confidence sets for a post-treatment effect under a sequence of restrictions on the possible difference in trends.

Two restriction families are supported:

• "relative_magnitude" ($\Delta^{RM}(\bar M)$): post-treatment violations are at most $\bar M$ times the largest pre-treatment violation (Rambachan & Roth 2023, Section 2.4.1).

• "smoothness" ($\Delta^{SD}(M)$): the difference in trends deviates from linearity by at most $M$ per period (Section 2.4.3).

Inference uses the Andrews-Roth-Pakes (ARP) conditional moment-inequality test (Section 3.2.1), which is uniformly valid and recommended for general restriction sets.

Usage

honest_sensitivity(
  object = NULL,
  type = c("relative_magnitude", "smoothness"),
  m_grid = NULL,
  l_vec = NULL,
  alpha = 0.05,
  grid_points = 1000L,
  betahat = NULL,
  sigma = NULL,
  n_pre_periods = NULL,
  n_post_periods = NULL,
  Mvec = lifecycle::deprecated(),
  gridPoints = lifecycle::deprecated(),
  numPrePeriods = lifecycle::deprecated(),
  numPostPeriods = lifecycle::deprecated()
)

Arguments

Value

A data.frame of class "honest_result" with one row per restriction value plus the original (parallel-trends) confidence interval, with columns M, lb, ub, method, and type. The breakdown value (largest restriction at which the robust CI still excludes 0) is stored in attr(., "breakdown").

References

Rambachan, A. and Roth, J. (2023). A More Credible Approach to Parallel Trends. Review of Economic Studies, 90(5), 2555-2591.

See Also

event_study(), plot.honest_result()

Description

Visualises the full cohort-by-period ATT(g,t) matrix stored in the att_gt attribute of an es_result object produced by run_es(estimator = "cs"). Two display styles are available:

• "heatmap": a tile plot with calendar time $t$ on the x-axis and cohort $g$ on the y-axis, colour-filled by the point estimate. Cells whose pointwise confidence interval excludes zero are marked with a filled dot; cells that are simultaneously significant (when bootstrap data are available) are additionally marked with an open diamond.

• "facet": one panel per cohort showing ATT(g,t) over calendar time $t$ with a pointwise confidence ribbon, mirroring the style of plot_es(). A lighter simultaneous CI ribbon is overlaid when bootstrap data are available.

Both types draw a vertical dashed line at $t = g$ (treatment onset) for each cohort.

Usage

## S3 method for class 'att_gt_result'
plot(
  x,
  type = c("heatmap", "facet"),
  ci_level = 0.95,
  zero_line = TRUE,
  theme = c("bw", "minimal", "classic"),
  color = NULL,
  fill = NULL,
  alpha = NULL,
  ...
)

## S3 method for class 'att_gt_result'
autoplot(object, ...)

Arguments

Value

A ggplot2::ggplot() object.

Bootstrap annotation

When attr(x, "bootstrap") is present (i.e., run_es() was called with bootstrap = TRUE), both plot types add simultaneous inference overlays sourced from the (g,t)-level bootstrap object.

See Also

plot_es(), run_es()

Examples

## Not run: 
cs_result <- event_study(y ~ 1 | id + year, data = mydata, time = year,
                         timing = g, estimator = "cs")
plot(att_gt(cs_result))
plot(att_gt(cs_result), type = "facet")

## End(Not run)

Description

Base plot() method for att_result objects returned by att() (or the deprecated calc_att()). Draws point estimates with confidence-interval bars: a single point for aggregation = "simple", one point per cohort for "by_cohort", and one per calendar period for "by_time".

Usage

## S3 method for class 'att_result'
plot(
  x,
  ci_level = 0.95,
  color = NULL,
  zero_line = TRUE,
  theme_style = c("bw", "minimal", "classic"),
  ...
)

## S3 method for class 'att_result'
autoplot(object, ...)

Arguments

Value

A ggplot object.

See Also

att()

Examples

## Not run: 
res <- att(y ~ 1 | id + year, data = df, time = year, timing = g,
           aggregation = "by_cohort")
plot(res)

## End(Not run)

Description

Base plot() method for es_result objects returned by event_study() (or the deprecated run_es()). Draws the event-study curve with pointwise confidence bands as a static ggplot, or — with interactive = TRUE — an interactive plotly chart with hover tooltips (requires the suggested {plotly} package).

Usage

## S3 method for class 'es_result'
plot(
  x,
  ci_level = 0.95,
  type = c("errorbar", "ribbon"),
  time_axis = c("relative", "calendar"),
  interactive = FALSE,
  show_simultaneous = FALSE,
  ...
)

Arguments

Details

The default display is black error bars. type = "ribbon" switches to a line with a shaded band and, unless color/fill say otherwise, to the package's muted steel-blue palette. In either display color sets the points (and the line), while errorbar_color can recolour the bars on their own.

time_axis controls what the horizontal axis shows. The default "relative" plots event time (periods since treatment) and labels the axis "Relative Time to Treatment". "calendar" instead plots the original time values (e.g. 2010, or a Date), labels the axis with the time column's name, and moves the dashed vertical reference line to the treatment period. It is available whenever every treated unit adopts in the same period — universal timing, or a staggered design that happens to have a single cohort — and errors otherwise, because with several cohorts one relative period corresponds to several calendar periods.

Value

A ggplot object, or a plotly object when interactive = TRUE.

See Also

event_study(), autoplot.es_result()

Examples

## Not run: 
res <- event_study(y ~ 1 | id + year, data = df, time = year, timing = g,
                   estimator = "cs")
plot(res)
plot(res, type = "ribbon", ci_level = 0.9)
plot(res, color = "#1B4965", errorbar_color = "grey50")
plot(res, interactive = TRUE)

# Universal timing: label the axis with calendar years instead
uni <- event_study(y ~ d | id + year, data = df, time = year,
                   timing = 2010)
plot(uni, time_axis = "calendar")

## End(Not run)

Description

Visualises the output of honest_sensitivity(): robust confidence intervals for the target effect under progressively weaker parallel-trends restrictions (increasing $M$ or $\bar M$), alongside the original confidence interval that assumes parallel trends holds exactly. This is the "top-down" sensitivity plot of Rambachan and Roth (2023).

Usage

## S3 method for class 'honest_result'
plot(x, ...)

## S3 method for class 'honest_result'
autoplot(object, ...)

Arguments

Value

A ggplot object.

See Also

honest_sensitivity()

Description

Base plot() method for the contamination weights returned by contamination_weights(): a ggplot2 tile heatmap.

Each cell at position (twfe_period, catt_label) shows the weight $\omega^{\ell''}_{e,\ell}$: how much of $CATT_{e,\ell}$ leaks into the TWFE coefficient $\hat\mu_{\ell''}$.

• Diagonal cells (catt_period == twfe_period): own-period weights (sum across cohorts should be $\approx 1$).

• Off-diagonal cells: cross-period contamination (ideally close to zero under treatment effect homogeneity).

Usage

## S3 method for class 'sa_contamination_weights'
plot(
  x,
  limit_abs = NULL,
  midpoint = 0,
  low = "#2166AC",
  mid = "white",
  high = "#B2182B",
  theme = c("bw", "minimal", "classic"),
  show_values = FALSE,
  value_digits = 2L,
  ...
)

Arguments

Value

A ggplot2::ggplot() object.

See Also

contamination_weights()

Description

Returns a tidy data frame of the aggregated ATT estimates from a att() result, in the column format expected by modelsummary::modelsummary() (and therefore renderable with tinytable::tt()). This makes it easy to put overall, by-cohort, or by-time ATTs into a publication table, and to stack several estimators (e.g. did() TWFE, CS, and BJS) side by side in one table.

The term column is derived from the aggregation type so that rows align across models:

• aggregation = "simple" -> "ATT"

• aggregation = "by_cohort" -> "Cohort <g>"

• aggregation = "by_time" -> "Time <t>"

Usage

## S3 method for class 'att_result'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

Arguments

Value

A data frame with columns term, estimate, std.error, statistic, p.value (and optionally conf.low, conf.high).

Examples

## Not run: 
res <- att(y ~ D | id + year, data = df, time = year,
           estimator = "cs", aggregation = "by_cohort")
broom::tidy(res)

# Compare estimators in one table:
twfe <- did(y ~ D | id + year, data = df)
cs   <- att(y ~ D | id + year, data = df, time = year, estimator = "cs")
bjs  <- att(y ~ D | id + year, data = df, time = year, estimator = "bjs")
modelsummary::modelsummary(list(TWFE = twfe, CS = cs, BJS = bjs))

## End(Not run)

Description

Returns a tidy data frame of model coefficients from a did() result: all regressors (treatment and covariates), in the format expected by modelsummary::modelsummary(). The table is read from the fit stored at estimation time; the VCOV type chosen in did() (vcov / cluster) is already reflected in the standard errors.

Usage

## S3 method for class 'did_result'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

Arguments

Value

A tibble with columns term, estimate, std.error, statistic, p.value (and optionally conf.low, conf.high).

Examples

## Not run: 
res <- did(y ~ D | id + year, data = df)
broom::tidy(res)
broom::tidy(res, conf.int = TRUE)

## End(Not run)

Description

Returns the event-study coefficient table of an event_study() result as a plain tibble — the format expected by modelsummary::modelsummary() and other broom-aware tooling. The baseline row (zero by construction) is included; drop it with x[!x$is_baseline, ] beforehand if undesired.

Usage

## S3 method for class 'es_result'
tidy(x, ...)

Arguments

Value

A tibble with columns term, estimate, std.error, statistic, p.value, relative_time, is_baseline, and the conf_low_XX/conf_high_XX pairs of the originating call.

Examples

## Not run: 
res <- event_study(y ~ 1 | id + year, data = df, time = year, timing = g,
                   estimator = "cs")
broom::tidy(res)

## End(Not run)